Resource- and Time-Constrained Control Synthesis for Multi-Agent Systems

Abstract: Multi-agent systems are employed for a group of agents to achieve coordinated tasks, in which distributed sensing, computing, communication and control are usually integrated with shared resources. Efficient usage of these resources is therefore an important issue. In addition, in applications such as robotics, a group of agents may encounter the request of a sequence of tasks and deadline constraint on the completion of each task is a common requirement. Thus, the integration of multi-agent task scheduling and control synthesis is of great practical interest. In this thesis, we study control of multi-agent systems under a networked control system framework. The first purpose is to design resource-efficient communication and control strategies to solve consensus problem for multi-agent systems.The second purpose is to jointly schedule task sequence and design controllers for multiagent systems that are subject to a sequence of deadline-constrained tasks. In the first part, a distributed asynchronous event-triggered communication and control strategy is proposed to tackle multi-agent consensus. It is shown that the proposed event-triggered communication and control strategy fulfils the reduction of both the rates of sensor-controller communication and controller-actuator communication as well as excluding Zeno behavior. To further relax the requirement of continuous sensing and computing, a periodic event-triggered communication and control strategy is proposed in the second part. In addition, an observer-based encoder-decoder with finite-level quantizeris designed to deal with the constraint of limited data rate. An explicit formula for the maximum allowable sampling period is derived first. Then, it is proven that exponential consensus can be achieved in the presence of data rate constraint. Finally, in the third part, the problem of deadline-constrained multi-agent task scheduling and control synthesis is addressed. A dynamic scheduling strategy is proposed and a distributed hybrid control law is designed for each agent that guarantees the completion and deadline satisfaction of each task. The effectiveness of the theoretical results in the thesis is verified by several simulation examples.